DI-NIDS: Domain invariant network intrusion detection system

Author:

Layeghy SiamakORCID,Baktashmotlagh Mahsa,Portmann Marius

Funder

Queensland Government

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference39 articles.

1. Benchmarking the benchmark - analysis of synthetic NIDS datasets;Layeghy,2021

2. S.M. Erfani, M. Baktashmotlagh, M. Moshtaghi, V. Nguyen, C. Leckie, J. Bailey, K. Ramamohanarao, Robust Domain Generalisation by Enforcing Distribution Invariance, in: IJCAI International Joint Conference on Artificial Intelligence, Volume 2016-Janua,2016, pp. 1455–1461.

3. S. Al-riyami, F. Coenen, A. Lisitsa, A Re-evaluation of Intrusion Detection Accuracy : an Alternative Evaluation Strategy, in: ACM SIGSAC Conference on Computer and Communications Security, 2018, pp. 2195–2197.

4. Explainable cross-domain evaluation of ML-based network intrusion detection systems;Layeghy,2023

5. Detecting anomalies in network systems by leveraging neural networks;Hashemi,2021

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